In this paper we propose to model the dependence of multiple time series returns with a multivariate extension of the generalized secant hyperbolic distribution (GSH) using the NORTA (NORmal-to-Anything) approach and the Koehler and Symanowski copula function. The two methodologies permit to generate random vectors with marginals dis- tributed as a GSH distribution and given correlation matrix, which can be used to measure the risk of a portfolio using the Monte Carlo method.

Palmitesta, P. (2008). Risk measures with Generalized Secant Hyperbolic Dependence.

Risk measures with Generalized Secant Hyperbolic Dependence

PALMITESTA, PAOLA
2008

Abstract

In this paper we propose to model the dependence of multiple time series returns with a multivariate extension of the generalized secant hyperbolic distribution (GSH) using the NORTA (NORmal-to-Anything) approach and the Koehler and Symanowski copula function. The two methodologies permit to generate random vectors with marginals dis- tributed as a GSH distribution and given correlation matrix, which can be used to measure the risk of a portfolio using the Monte Carlo method.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/429386